APC/C Dysfunction Limits Excessive Cancer Chromosomal Instability
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Polyclonal Antibody to APC11 / ANAPC11 - Serum
OriGene Technologies, Inc. OriGene Technologies GmbH 9620 Medical Center Drive, Ste 200 Schillerstr. 5 Rockville, MD 20850 32052 Herford UNITED STATES GERMANY Phone: +1-888-267-4436 Phone: +49-5221-34606-0 Fax: +1-301-340-8606 Fax: +49-5221-34606-11 [email protected] [email protected] R1503 Polyclonal Antibody to APC11 / ANAPC11 - Serum Alternate names: Anaphase-promoting complex subunit 11, Cyclosome subunit 11, HSPC214, Hepatocellular carcinoma-associated RING finger protein Quantity: 0.1 ml Concentration: 85 mg/ml (by Refractometry) Background: APC11 is also known as Anaphase promoting complex subunit 11, APC11, Cyclosome subunit 11, Hepatocellular carcinoma associated RING finger protein, and HSPC214. APC11 is a component of the anaphase promoting complex/cyclosome (APC/C), a cell cycle-regulated E3 ubiquitin ligase that controls progression through mitosis and the G1 phase of the cell cycle. APC11 may function to recruit the E2 ubiquitin-conjugating enzymes to the complex. APC11 interacts with the cullin domain of ANAPC2 and also interacts with UBE2D2. APC11 shows both a cytoplasmic and nuclear localization. APC11 is expressed at high levels in skeletal muscle and heart; in moderate levels in brain, kidney, and liver; and at low levels in colon, thymus, spleen, small intestine, placenta, lung and peripheral blood leukocyte. APC11 is a member of the RING-type zinc finger family and is auto-ubiquitinylated. Uniprot ID: Q9NYG5 NCBI: NP_001002244.1 GeneID: 51529 Host: Rabbit Immunogen: This APC11 antibody was prepared from whole rabbit serum produced by repeated immunizations with a synthetic peptide corresponding to amino acids 76-84 of Human APC11 (C-terminal) coupled to KLH. -
A New Genetic Method for Isolating Functionally Interacting Genes
Copyright 2000 by the Genetics Society of America A New Genetic Method for Isolating Functionally Interacting Genes: High plo1؉-Dependent Mutants and Their Suppressors De®ne Genes in Mitotic and Septation Pathways in Fission Yeast C. Fiona Cullen,*,² Karen M. May,* Iain M. Hagan,³ David M. Glover²,§ and Hiroyuki Ohkura*,² *Institute of Cell and Molecular Biology, The University of Edinburgh, Edinburgh EH9 3JR, United Kingdom, ²Department of Anatomy and Physiology, Medical Sciences Institute, The University of Dundee, Dundee DD1 4HN, United Kingdom, ³School of Biological Sciences, The University of Manchester, Manchester M13 9PT, United Kingdom and §Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom Manuscript received February 2, 2000 Accepted for publication April 10, 2000 ABSTRACT We describe a general genetic method to identify genes encoding proteins that functionally interact with and/or are good candidates for downstream targets of a particular gene product. The screen identi®es mutants whose growth depends on high levels of expression of that gene. We apply this to the plo1ϩ gene that encodes a ®ssion yeast homologue of the polo-like kinases. plo1ϩ regulates both spindle formation and septation. We have isolated 17 high plo1ϩ-dependent (pld) mutants that show defects in mitosis or septation. Three mutants show a mitotic arrest phenotype. Among the 14 pld mutants with septation defects, 12 mapped to known loci: cdc7, cdc15, cdc11 spg1, and sid2. One of the pld mutants, cdc7-PD1, was selected for suppressor analysis. As multicopy suppressors, we isolated four known genes involved in septation in ®ssion yeast: spg1ϩ, sce3ϩ, cdc8ϩ, and rho1ϩ, and two previously uncharacterized genes, mpd1ϩ and mpd2ϩ. -
Apc11 (ANAPC11) (NM 001002245) Human Tagged ORF Clone Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC223841L4 Apc11 (ANAPC11) (NM_001002245) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: Apc11 (ANAPC11) (NM_001002245) Human Tagged ORF Clone Tag: mGFP Symbol: ANAPC11 Synonyms: APC11; Apc11p; HSPC214 Vector: pLenti-C-mGFP-P2A-Puro (PS100093) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: Puromycin ORF Nucleotide The ORF insert of this clone is exactly the same as(RC223841). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_001002245 ORF Size: 252 bp This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 Apc11 (ANAPC11) (NM_001002245) Human Tagged ORF Clone – RC223841L4 OTI Disclaimer: Due to the inherent nature of this plasmid, standard methods to replicate additional amounts of DNA in E. coli are highly likely to result in mutations and/or rearrangements. Therefore, OriGene does not guarantee the capability to replicate this plasmid DNA. Additional amounts of DNA can be purchased from OriGene with batch-specific, full-sequence verification at a reduced cost. Please contact our customer care team at [email protected] or by calling 301.340.3188 option 3 for pricing and delivery. The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. -
Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice
Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications 2013 Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice Mihaela Palicev Gunter P. Wagner James P. Noonan Benedikt Hallgrimsson James M. Cheverud Loyola University Chicago, [email protected] Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs Part of the Biology Commons Recommended Citation Palicev, M, GP Wagner, JP Noonan, B Hallgrimsson, and JM Cheverud. "Genomic Correlates of Relationship QTL Involved in Fore- Versus Hind Limb Divergence in Mice." Genome Biology and Evolution 5(10), 2013. This Article is brought to you for free and open access by the Faculty Publications at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. © Palicev et al., 2013. GBE Genomic Correlates of Relationship QTL Involved in Fore- versus Hind Limb Divergence in Mice Mihaela Pavlicev1,2,*, Gu¨ nter P. Wagner3, James P. Noonan4, Benedikt Hallgrı´msson5,and James M. Cheverud6 1Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria 2Department of Pediatrics, Cincinnati Children‘s Hospital Medical Center, Cincinnati, Ohio 3Yale Systems Biology Institute and Department of Ecology and Evolutionary Biology, Yale University 4Department of Genetics, Yale University School of Medicine 5Department of Cell Biology and Anatomy, The McCaig Institute for Bone and Joint Health and the Alberta Children’s Hospital Research Institute for Child and Maternal Health, University of Calgary, Calgary, Canada 6Department of Anatomy and Neurobiology, Washington University *Corresponding author: E-mail: [email protected]. -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Protein Interaction Network of Alternatively Spliced Isoforms from Brain Links Genetic Risk Factors for Autism
ARTICLE Received 24 Aug 2013 | Accepted 14 Mar 2014 | Published 11 Apr 2014 DOI: 10.1038/ncomms4650 OPEN Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism Roser Corominas1,*, Xinping Yang2,3,*, Guan Ning Lin1,*, Shuli Kang1,*, Yun Shen2,3, Lila Ghamsari2,3,w, Martin Broly2,3, Maria Rodriguez2,3, Stanley Tam2,3, Shelly A. Trigg2,3,w, Changyu Fan2,3, Song Yi2,3, Murat Tasan4, Irma Lemmens5, Xingyan Kuang6, Nan Zhao6, Dheeraj Malhotra7, Jacob J. Michaelson7,w, Vladimir Vacic8, Michael A. Calderwood2,3, Frederick P. Roth2,3,4, Jan Tavernier5, Steve Horvath9, Kourosh Salehi-Ashtiani2,3,w, Dmitry Korkin6, Jonathan Sebat7, David E. Hill2,3, Tong Hao2,3, Marc Vidal2,3 & Lilia M. Iakoucheva1 Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. -
SETD2 Haploinsufficiency for Microtubule Methylation Is an Early Driver of Genomic Instability in Renal Cell Carcinoma
Published OnlineFirst May 3, 2018; DOI: 10.1158/0008-5472.CAN-17-3460 Cancer Genome and Epigenome Research SETD2 Haploinsufficiency for Microtubule Methylation Is an Early Driver of Genomic Instability in Renal Cell Carcinoma Yun-Chen Chiang1, In-Young Park2, Esteban A. Terzo3, Durga Nand Tripathi2, Frank M. Mason3, Catherine C. Fahey1, Menuka Karki2,4, Charles B. Shuster4, Bo-Hwa Sohn2, Pratim Chowdhury2, Reid T. Powell5, Ryoma Ohi6, Yihsuan S. Tsai1, Aguirre A. de Cubas3, Abid Khan1,7, Ian J. Davis1, Brian D. Strahl1,7, Joel S. Parker1, Ruhee Dere2, Cheryl L. Walker2, and W. Kimryn Rathmell3 Abstract Loss of the short arm of chromosome 3 (3p) occurs early in human kidney cells, rescue with a pathogenic SETD2 mutant >95% of clear cell renal cell carcinoma (ccRCC). Nearly ubiqui- deficient for microtubule (aTubK40me3), but not histone tous 3p loss in ccRCC suggests haploinsufficiency for 3p tumor (H3K36me3) methylation, replicated this phenotype. Genomic suppressors as early drivers of tumorigenesis. We previously instability (micronuclei) was also a hallmark of patient-derived reported methyltransferase SETD2, which trimethylates H3 his- cells from ccRCC. These data show that the SETD2 tumor sup- tones on lysine 36 (H3K36me3) and is located in the 3p deletion, pressor displays a haploinsufficiency phenotype disproportion- to also trimethylate microtubules on lysine 40 (aTubK40me3) ately impacting microtubule methylation and serves as an early during mitosis, with aTubK40me3 required for genomic sta- driver of genomic instability. bility. We now show that monoallelic, Setd2-deficient cells retain- Significance: Loss of a single allele of a chromatin modifier ing H3K36me3, but not aTubK40me3, exhibit a dramatic plays a role in promoting oncogenesis, underscoring the grow- increase in mitotic defects and micronuclei count, with increased ing relevance of tumor suppressor haploinsufficiency in tumor- viability compared with biallelic loss. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Possible Hazards of Cell Phones and Towers, Wi-Fi, Smart Meters, and Wireless Computers, Printers, Laptops, Mice, Keyboards, and Routers Book Four
Possible Hazards of Cell Phones and Towers, Wi-Fi, Smart Meters, and Wireless Computers, Printers, Laptops, Mice, Keyboards, and Routers Book Four Since 2013 I have been emailed several dozen reports of possible medical and other hazards from intense electromagnetic radiation from cell phones and towers, Wi-Fi, smart meters, and wireless computer accessories including wireless computers, keyboards, mice, routers, printers, and laptops. I have previously compiled a total of 600 pages of these reports in chronological order in three separate books with the same title as this “Book Four”. All four ‘EMF Hazards’ books are linked at www.commutefaster.com/vesperman.html and www.padrak.com/vesperman. Approximately 35 authoritative wireless radiation hazards-related reports are also linked at these two websites. This report begins with “Disclaimers”, a table of contents, “Items of Outstanding Interest”, and a new supplementary set of potentially useful “Recommendations for Actions”. Gary Vesperman 588 Lake Huron Lane Boulder City, NV 89005-1018 702-435-7947 [email protected] Hazards of Cell Phones, Wireless Devices, Etc – Book Four 1 December 14, 2016 Disclaimers Inclusion of any invention or technology in this “Possible Hazards of Cell Phones and Towers, Wi-Fi, Smart Meters, and Wireless Computers, Printers, Laptops, Mice, Keyboards, and Routers – Book Four” does not in any way imply its suitability for investments of any kind. Nor does inclusion of any invention or technology described or mentioned herein conclusively implies safety or hazards. Gary C. Vesperman, Boulder City, Nevada and the numerous contributors to this compilation do not warrant that any of the information presented is accurate, complete, and not misleading. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/)